Power system operators must ensure that dispatch decisions remain feasible in case of grid outages or contingencies to prevent cascading failures and ensure reliable operation. However, checking the feasibility of all $N - k$ contingencies -- every possible simultaneous failure of $k$ grid components -- is computationally intractable for even small $k$, requiring system operators to resort to heuristic screening methods. Because of the increase in uncertainty and changes in system behaviors, heuristic lists might not include all relevant contingencies, generating false negatives in which unsafe scenarios are misclassified as safe. In this work, we propose to use input-convex neural networks (ICNNs) for contingency screening. We show that ICNN reliability can be determined by solving a convex optimization problem, and by scaling model weights using this problem as a differentiable optimization layer during training, we can learn an ICNN classifier that is both data-driven and has provably guaranteed reliability. Namely, our method can ensure a zero false negative rate. We empirically validate this methodology in a case study on the IEEE 39-bus test network, observing that it yields substantial (10-20x) speedups while having excellent classification accuracy.

本研究解决了电力系统在网格故障时对 $N-k$ 应急情况的可行性检查问题。通过引入输入凸神经网络（ICNNs），我们提出了一种新颖的方法来确保零假阴性率，从而增强了应急筛查的可靠性和效率。实验证明，该方法在IEEE 39节点测试网络中实现了10至20倍的速度提升，同时保持了出色的分类准确率。

快速且可靠的 $N-k$ 应急筛查：输入凸神经网络的应用